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funasr_local/runtime/triton_gpu/client/client.py

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+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import multiprocessing
+from multiprocessing import Pool
+
+import argparse
+import os
+import tritonclient.grpc as grpcclient
+from utils import cal_cer
+from speech_client import *
+import numpy as np
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-v",
+        "--verbose",
+        action="store_true",
+        required=False,
+        default=False,
+        help="Enable verbose output",
+    )
+    parser.add_argument(
+        "-u",
+        "--url",
+        type=str,
+        required=False,
+        default="localhost:10086",
+        help="Inference server URL. Default is " "localhost:8001.",
+    )
+    parser.add_argument(
+        "--model_name",
+        required=False,
+        default="attention_rescoring",
+        choices=["attention_rescoring", "streaming_wenet", "infer_pipeline"],
+        help="the model to send request to",
+    )
+    parser.add_argument(
+        "--wavscp",
+        type=str,
+        required=False,
+        default=None,
+        help="audio_id \t wav_path",
+    )
+    parser.add_argument(
+        "--trans",
+        type=str,
+        required=False,
+        default=None,
+        help="audio_id \t text",
+    )
+    parser.add_argument(
+        "--data_dir",
+        type=str,
+        required=False,
+        default=None,
+        help="path prefix for wav_path in wavscp/audio_file",
+    )
+    parser.add_argument(
+        "--audio_file",
+        type=str,
+        required=False,
+        default=None,
+        help="single wav file path",
+    )
+    # below arguments are for streaming
+    # Please check onnx_config.yaml and train.yaml
+    parser.add_argument("--streaming", action="store_true", required=False)
+    parser.add_argument(
+        "--sample_rate",
+        type=int,
+        required=False,
+        default=16000,
+        help="sample rate used in training",
+    )
+    parser.add_argument(
+        "--frame_length_ms",
+        type=int,
+        required=False,
+        default=25,
+        help="frame length",
+    )
+    parser.add_argument(
+        "--frame_shift_ms",
+        type=int,
+        required=False,
+        default=10,
+        help="frame shift length",
+    )
+    parser.add_argument(
+        "--chunk_size",
+        type=int,
+        required=False,
+        default=16,
+        help="chunk size default is 16",
+    )
+    parser.add_argument(
+        "--context",
+        type=int,
+        required=False,
+        default=7,
+        help="subsampling context",
+    )
+    parser.add_argument(
+        "--subsampling",
+        type=int,
+        required=False,
+        default=4,
+        help="subsampling rate",
+    )
+
+    FLAGS = parser.parse_args()
+    print(FLAGS)
+
+    # load data
+    filenames = []
+    transcripts = []
+    if FLAGS.audio_file is not None:
+        path = FLAGS.audio_file
+        if FLAGS.data_dir:
+            path = os.path.join(FLAGS.data_dir, path)
+        if os.path.exists(path):
+            filenames = [path]
+    elif FLAGS.wavscp is not None:
+        audio_data = {}
+        with open(FLAGS.wavscp, "r", encoding="utf-8") as f:
+            for line in f:
+                aid, path = line.strip().split("\t")
+                if FLAGS.data_dir:
+                    path = os.path.join(FLAGS.data_dir, path)
+                audio_data[aid] = {"path": path}
+        with open(FLAGS.trans, "r", encoding="utf-8") as f:
+            for line in f:
+                aid, text = line.strip().split("\t")
+                audio_data[aid]["text"] = text
+        for key, value in audio_data.items():
+            filenames.append(value["path"])
+            transcripts.append(value["text"])
+
+    num_workers = multiprocessing.cpu_count() // 2
+
+    if FLAGS.streaming:
+        speech_client_cls = StreamingSpeechClient
+    else:
+        speech_client_cls = OfflineSpeechClient
+
+    def single_job(client_files):
+        with grpcclient.InferenceServerClient(
+            url=FLAGS.url, verbose=FLAGS.verbose
+        ) as triton_client:
+            protocol_client = grpcclient
+            speech_client = speech_client_cls(
+                triton_client, FLAGS.model_name, protocol_client, FLAGS
+            )
+            idx, audio_files = client_files
+            predictions = []
+            for li in audio_files:
+                result = speech_client.recognize(li, idx)
+                print("Recognized {}:{}".format(li, result[0]))
+                predictions += result
+        return predictions
+
+    # start to do inference
+    # Group requests in batches
+    predictions = []
+    tasks = []
+    splits = np.array_split(filenames, num_workers)
+
+    for idx, per_split in enumerate(splits):
+        cur_files = per_split.tolist()
+        tasks.append((idx, cur_files))
+
+    with Pool(processes=num_workers) as pool:
+        predictions = pool.map(single_job, tasks)
+
+    predictions = [item for sublist in predictions for item in sublist]
+    if transcripts:
+        cer = cal_cer(predictions, transcripts)
+        print("CER is: {}".format(cer))